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Stop treating LLM apps like standard software. 🛑 Many teams are trying to force-fit DevOps practices onto GenAI pipelines. But DevOps, MLOps, and LLMOps solve fundamentally different problems. Here is the breakdown of the evolution: 1️⃣ DevOps is Software-Centric The feedback loop is deterministic: Does the code build? Do the tests pass? The artifacts are code. The tooling is mature. 2️⃣ MLOps is (Model Data) Centric Now you have to handle data drift and model decay. The code might be fine, but
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DevOps vs. MLOps vs. LLMOps:Many teams are trying to apply DevOps practices to LLM apps.But DevOps, MLOps, and LLMOps solve fundamentally different problems.DevOps is software-centric. You write code, test it, and deploy it. The feedback loop is straightforward: Does the code work or not?MLOps is model-centric. Here, you're dealing with data drift, model decay, and continuous retraining. The code might be fine, but the model's performance can degrade over time because the world changes.LLMOps is
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